| dc.description.abstract | Although the Multi-Input and Multi-Output (MIMO) communication systems provide
very high data rates with low error probabilities, these advantages are obtained
at the expense of having high signal processing tasks and the hardware cost,
e.g. expensive Analog-to-Digital (A/D) converters. The increased hardware cost
is mainly due to having multiple Radio Frequency (RF) chains (one for each antenna
element). Antenna selection techniques have been proposed to lower the
number of RF chains and provide a low cost MIMO system. Among them, due to a
beamforming capability Soft Antenna Selection (SAS) schemes have shown a great
performance improvement against the traditional antenna sub-set selection methods
for the MIMO communication systems with the same number of RF chains.
A SAS method is basically realized by a pre-processing module which is located
in RF domain of a MIMO system. In this thesis, we investigate on the receive
SAS-MIMO, i.e. a MIMO system equipped with a SAS module at the receiver side,
in noise-limited/interference channels. For a noise-limited channel, we study the
SAS-MIMO system for when the SAS module is implemented before Low Noise
Amplifier (LNA), so-called pre-LNA, under both spatial multiplexing and diversity
transmission strategies. The pre-LNA SAS module only consists of passive
elements. The optimality of the pre-LNA SAS method is investigated under two
di erent practical cases of either the external or internal noise dominates. For the
interference channel case, the post-LNA SAS scheme is optimized based on Power
Angular Spectrum (PAS) of the received interference signals. The analytical derivations
for both noise-limited and interference channels are verified via the computer
simulations based on a general Rician statistical MIMO channel model. The simulation
results reveal a superiority of the post-LNA SAS to the post-LNA SAS at any
condition. Moreover, using the simulations performed for the interference channels
we show that the post-LNA SAS is upper bounded by the full-complexity MIMO.
Since in both above-mentioned channels, noise-limited and interference, the
channel knowledge is needed for the SAS optimization, in this thesis we also propose
a two-step channel estimation method for the SAS-MIMO. This channel estimation
is based on an Orthogonal Frequency-Division Multiplexing (OFDM) MIMO system.
Two di erent estimators of Least-Square (LS) and Minimum-Mean-Square-
Error (MMSE) are applied. Simulation results show a superiority of the MMSE
method to the LS estimator for a MIMO system simulated under the 802.16 framing
strategy. Moreover, a 802.11a framing based SAS-MIMO is simulated using
MATLAB SIMULINK to verify the two-step estimation procedure.
Furthermore, we also employ a ray-tracing channel simulation to assess di erent
SAS configurations, i.e. realized by active (post-LNA) and/or passive (pre-LNA)
phased array, in terms of signal coverage. In this regard, a rigorous Signal to Noise
Ratio (SNR) analysis is performed for each of these SAS realizations. The results
show that although the SAS method performance is generally said to be upperbounded
by a full-complexity MIMO, it shows a better signal coverage than the
full-complexity MIMO. | en |
